Advertisement

Nesting OpenMP in MPI to Implement a Hybrid Communication Method of Parallel Simulated Annealing on a Cluster of SMP Nodes

  • Agnieszka Debudaj-Grabysz
  • Rolf Rabenseifner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3666)

Abstract

Concurrent computing can be applied to heuristic methods for combinatorial optimization to shorten computation time, or equivalently, to improve the solution when time is fixed. This paper presents several communication schemes for parallel simulated annealing, focusing on a combination of OpenMP nested in MPI. Strikingly, even though many publications devoted to either intensive or sparse communication methods in parallel simulated annealing exist, only a few comparisons of methods from these two distinctive families have been published; the present paper aspires to partially fill this gap. Implementation for VRPTW—a generally accepted benchmark problem—is used to illustrate the advantages of the hybrid method over others tested.

Keywords

Parallel processing MPI OpenMP communication simulated annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aarts, E., de Bont, F., Habers, J., van Laarhoven, P.: Parallel implementations of the statistical cooling algorithm. Integration, the VLSI journal, 209–238 (1986)Google Scholar
  2. 2.
    Aarts, E., Korst, J.: Simulated Annealing and Boltzman Machines. John Wiley & Sons, Chichester (1989)Google Scholar
  3. 3.
    Azencott, R. (ed.): Simulated Annealing Parallelization Techniques. John Wiley & Sons, New York (1992)zbMATHGoogle Scholar
  4. 4.
    Arbelaitz, O., Rodriguez, C., Zamakola, I.: Low Cost Parallel Solutions for the VRPTW Optimization Problem. In: Proceedings of the International Conference on Parallel Processing Workshops, pp. 176–181. IEEE Computer Society, Valencia–Spain (2001)CrossRefGoogle Scholar
  5. 5.
    Czarnas, P.: Traveling Salesman Problem With Time Windows. Solution by Simulated Annealing. MSc thesis (in Polish), Uniwersytet Wrocławski, Wrocław (2001)Google Scholar
  6. 6.
    Czech, Z.J., Czarnas, P.: Parallel simulated annealing for the vehicle routing problem with time windows. In: 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing, Canary Islands–Spain, pp. 376–383 (2002)Google Scholar
  7. 7.
    Debudaj-Grabysz, A., Czech, Z.J.: A concurrent implementation of simulated annealing and its application to the VRPTW optimization problem. In: Juhasz, Z., Kacsuk, P., Kranzlmuller, D. (eds.) Distributed and Parallel Systems. Cluster and Grid Computing, vol. 777, pp. 201–209. Kluwer International Series in Engineering and Computer Science, Dordrecht (2004)Google Scholar
  8. 8.
    Greening, D.R.: Parallel Simulated Annealing Techniques. Physica D 42, 293–306 (1990)CrossRefGoogle Scholar
  9. 9.
    Gropp, W., Lusk, E., Doss, N., Skjellum, A.: A high-performance, portable implementation of the MPI message passing interface standard. Parallel Computing 22(6), 789–828 (1996)zbMATHCrossRefGoogle Scholar
  10. 10.
    Lee, F.A.: Parallel Simulated Annealing on a Message-Passing Multi-Computer. PhD thesis, Utah State University (1995)Google Scholar
  11. 11.
    Lee, K.–G., Lee, S.–Y.: Synchronous and Asynchronous Parallel Simulated Annealing with Multiple Markov Chains. IEEE Transactions on Parallel and Distributed Systems 7(10), 993–1008 (1996)CrossRefGoogle Scholar
  12. 12.
    OpenMP C and C++ API 2.0 Specification, from www.openmp.org/specs/
  13. 13.
    Onbaoglu, E., Özdamar, L.: Parallel Simulated Annealing Algorithms in Global Optimization. Journal of Global Optimization 19(1), 27–50 (2001)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Solomon, M.: Algorithms for the vehicle routing and scheduling problem with time windows constraints. Operation Research 35, 254–265 (1987), see also http://w.cba.neu.edu/~msolomon/problems.htm zbMATHCrossRefGoogle Scholar
  15. 15.
    Salamon, P., Sibani, P., Frost, R.: Facts, Conjectures and Improvements for Simulated Annealing. SIAM (2002)Google Scholar
  16. 16.
    Tan, K.C., Lee, L.H., Zhu, Q.L., Ou, K.: Heuristic methods for vehicle routing problem with time windows. In: Artificial Intelligent in Engineering, pp. 281–295. Elsevier, Amsterdam (2001)Google Scholar
  17. 17.
    Zomaya, A.Y., Kazman, R.: Simulated Annealing Techniques. In: Algorithms and Theory of Computation Handbook. CRC Press LLC, Boca Raton (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Agnieszka Debudaj-Grabysz
    • 1
  • Rolf Rabenseifner
    • 2
  1. 1.Department of Computer ScienceSilesian University of TechnologyGliwicePoland
  2. 2.High-Performance Computing-Center (HLRS)University of StuttgartStuttgartGermany

Personalised recommendations